Scope: Different "omics" data types are often analyzed separately to evaluate the effects of diet on biological processes. Combining these datasets in a single, integrated analysis may better characterize the effects of nutrition on human health. Methods and results: We investigated the performance of two data integration tools, "SNFtool" and "DIABLO" (MixOmics), in discriminating responses to diet and metabolic phenotypes by combining transcriptomics and metabolomics datasets from three human intervention studies: a postprandial crossover study testing dairy foods (n = 7; study 1), a postprandial challenge study comparing obese and non-obese subjects (n = 13; study 2), and an 8-week parallel intervention study that assessed three diets with variable lipid content on fasting parameters (n = 39; study 3). In study 1, combining datasets using SNF or DIABLO significantly improved sample classification. For studies 2 and 3, the value of SNF integration depended on the dietary groups being compared, while DIABLO discriminated samples well but did not perform better than transcriptomic data alone. Conclusion: The integration of associated "omics" datasets can help to clarify the subtle signals observed in nutritional interventions. The performance of each integration tool is differently influenced by study design, size of the datasets, and sample size. This article is protected by copyright. All rights reserved.
